Handling the peculiarities of marine movement data
Marie Auger-Méthé
1 Credits
This tutorial uses materials that was created by the CANSSI Collaborative Research Team led by Vianey Leos Barajas and Marie Auger-Méthé. I thank Fanny Dupont, Ron Togunov, Natasha Klappstein, Arturo Esquivel, Marco Gallegos Herrada, and Sofia Ruiz Suarez for their contribution to this original material.
2 Tutorial goals
The goal of this tutorial is to explore ways to pre-prepare marine movement data. The primary learning objectives are to:
- Regularize movement tracks that are irregular using Fastloc GPS data as an example.
- Create predicted tracks from error-prone data using Argos data as an example.
3 General setup
First, let’s load the packages that we will need to complete the analyses. Of course you need to have them installed first.
library(momentuHMM) # Package for fitting HMMs, we use it for crawlWrap
library(tidyverse) # data management
library(terra)
library(tidyterra)
library(ggspatial) # plot the data
library(sf) # spatial data processing
library(kableExtra) # produce visually appealing tables
library(geosphere) # to calculate step lengths from lat/lon locations - just need it installed
library(conicfit) # needs to be installed for crawlWrap
library(here) # To help with sourcing
library(adehabitatLT) # setNAWe are assuming that you are working from Madeira-Workshop repository main folder, and using here() to help find the good directory for each files.
We will need the functions in the following file utility_functions.R.
4 Regularising Fastloc GPS data
4.1 Narwhal movement data
We will analyze a dataset containing movement tracks of three narwhals tagged with Fastloc-GPS tags. The dataset was provided by Dr. Marianne Marcoux (Fisheries and Oceans, Canada). Dr. Marcoux provided the data only for this tutorial, please do not use for other purposes without their consent. Contact: Marianne.Marcoux@dfo-mpo.gc.ca.
For simplicity, we only examine the fastloc-GPS data from one week in August 2017.
